The invention discloses a ship bridge-crossing track prediction method, which comprises the following steps of: converting a time sequence of a track of each ship into an RGB (Red, Green, Blue) image by using a Grubrum angle field, then forming a training sample set by using the RGB image, and introducing a convolutional neural network and an attention mechanism into a current mature GAN (Graphics Area Network) structure to construct a ship bridge-crossing track prediction model. The convolutional neural network enables the ship bridge-crossing track prediction model to automatically learn features of an RGB image representing a ship track, and the attention mechanism enables the ship bridge-crossing track prediction model to fully utilize structural information in the RGB image, so that the ship bridge-crossing track prediction model can pay attention to an important long-distance dependency relationship in the RGB image. Key features in the RGB image are extracted more accurately; the method has the advantages that the prediction precision is high, and the requirements for model design and optimization are low.
本发明公开了一种船舶过桥航迹预测方法,使用格拉姆角场分别将每条船舶的航迹的时间序列转化为RGB图像后再采用RGB图像构成训练样本集,并在当前成熟的GAN网络结构中引入卷积神经网络和注意力机制来构建船舶过桥航迹预测模型,卷积神经网络使船舶过桥航迹预测模型可以自动学习表征船舶航迹的RGB图像的特征,注意力机制使船舶过桥航迹预测模型能够充分利用RGB图像中的结构信息,使得船舶过桥航迹预测模型能够关注RGB图像中重要的长距离依赖关系,更准确地提取RGB图像中的关键特征;优点是预测预测精度较高,且对于模型的设计和优化要求较低。
Ship bridge crossing track prediction method
一种船舶过桥航迹预测方法
2025-03-28
Patent
Electronic Resource
Chinese
IPC: | G06F ELECTRIC DIGITAL DATA PROCESSING , Elektrische digitale Datenverarbeitung / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen / G06V / G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS |
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